Pillar 01 · Cybersecurity
Secure Acceleration
Ship AI without opening a hole. I secure the systems, the data behind them, and the compliance story you will eventually be asked for.
What this is
The uncomfortable version: someone at your company has already pasted customer data into a chat tool, or wired an assistant into a system nobody reviewed. It is working well enough that nobody has stopped to ask what it can reach.
Secure Acceleration is the pillar that makes that safe without killing the momentum. I map what your AI systems can actually touch, close the exposure, and leave behind governance a normal employee will follow instead of route around.
This is not a generic security assessment with the word AI pasted on top. It is twenty years of cybersecurity and intelligence work applied to a class of system I build myself — which means I know exactly where these things leak, because I have had to plug those holes in my own products.
What I’ve shipped
- A multi-tenant compliance platform, working prototype. A platform for organizations that need tenant isolation and access controls to be verifiable, not just claimed: database-level tenant isolation, encryption in transit and at rest, mandatory multi-factor authentication with role-based views, and immutable read, write, export, and destruction logging. Core features run and are tested; not yet built out to a full production release.
- Guardrails and full tracing on a live AI product. Cast Sense refuses to answer past its sources, attributes what it does answer, and traces every model call through Langfuse — so a confidently wrong answer is something I find and fix, not something a customer discovers.
- Facility security assessment work under 33 CFR 105. Regulated-facility security assessment and plan work as a senior consultant, on real inspections with real regulators. No client or facility is ever named here.
What I’d build for you
- An AI exposure assessment: every model, agent, integration, and third-party tool touching your data, and what each one can actually reach.
- Prompt-injection and data-leak hardening on the systems you already run, with the test cases so you can prove it later.
- An AI governance policy short enough that people read it, plus an approval path for new tools that does not take three weeks.
- Retrieval and agent architecture that can refuse, attribute, and be audited — designed in, not bolted on.
- Compliance mapping for the workflows AI now touches, including regulated environments under MTSA and 33 CFR 104, 105, and 106.
